584 citations · 805 across the 20 of their papers we have counts for
7 papers · 1 filter
On the Feasibility of Load-Changing Attacks in Power Systems during the COVID-19 Pandemic
Juan Ospina, XiaoRui Liu, Charalambos Konstantinou +1
The electric power grid is a complex cyberphysical energy system (CPES) in which information and communication technologies (ICT) are integrated into the operations and services of…
To be Robust or to be Fair: Towards Fairness in Adversarial Training
Han Xu, Xiaorui Liu, Yaxin Li +2
Adversarial training algorithms have been proved to be reliable to improve machine learning models' robustness against adversarial examples. However, we find that adversarial train…
A Unified View on Graph Neural Networks as Graph Signal Denoising
Yao Ma, Xiaorui Liu, Tong Zhao +3
Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a…
A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systems
Ali Sayghe, Yaodan Hu, Ioannis Zografopoulos +4
Over the last decade, the number of cyberattacks targeting power systems and causing physical and economic damages has increased rapidly. Among them, False Data Injection Attacks (…
Deep Reinforcement Learning for Cybersecurity Assessment of Wind Integrated Power Systems
XiaoRui Liu, Juan Ospina, Charalambos Konstantinou
The integration of renewable energy sources (RES) is rapidly increasing in electric power systems (EPS). While the inclusion of intermittent RES coupled with the wide-scale deploym…
Linear Convergent Decentralized Optimization with Compression
Xiaorui Liu, Yao Li, Rongrong Wang +2
Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DG…
Graph Structure Learning for Robust Graph Neural Networks
Wei Jin, Yao Ma, Xiaorui Liu +3
Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, cal…